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Combined Set-Theoretic and Stochastic Estimation: A Comparison of the SSI and the CS Filter

Klumpp, Vesa; Noack, Benjamin; Baum, Marcus; Hanebeck, Uwe D.

Abstract:
In estimation theory, mainly set-theoretic or stochastic uncertainty is considered. In some cases, especially when some statistics of a distribution are not known or additional stochastic information is used in a set-theoretic estimator, both types of uncertainty have to be considered. In this paper, two estimators that cope with combined stoachastic and set-theoretic uncertainty are compared, namely the Set-theoretic and Statistical Information filter, which represents the uncertainty by means of random sets, and the Credal State filter, in which the state information is given by sets of probability density functions. The different uncertainty assessment in both estimators leads to different estimation results, even when the prior information and the measurement and system models are equal. This paper explains these differences and states directions, when which estimator should be applied to a given estimation problem.

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Volltext §
DOI: 10.5445/IR/1000035092
Coverbild
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2010
Sprache Englisch
Identifikator ISBN: 978-0-9824438-1-1
urn:nbn:de:swb:90-350923
KITopen-ID: 1000035092
Erschienen in Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, 26-29 July 2010
Verlag IEEE, Piscataway
Seiten 8 S.
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